Fake Currency Detection Using Neural Networks


Fake Currency Detection Using Neural Networks

This paper proposes an image processing technique to extract paper currency denominations. Automatic detection and recognition of Indian currency notes have gained a lot of research attention in recent years particularly due to its vast potential applications. It is shown that Indian currencies can be classified based on a set of unique nondiscriminating features. First, we acquire the image by the simple flat scanner on fixed dpi with a particular size, the pixels level is set to obtain mage. The dominant color and the aspect ratio of the note are extracted. After this extracted the portion of the note containing the unique shape, number, emblem, etc. This technique is used to match or find the currency denomination of paper currency.

Existing Systems

• In the existing system, classification is done through simple image processing to
classify based on color, shape, and other parameters.

• It has poor discriminatory power
• It is poor to characterize the excellence of data

Proposed system:
In this proposed system, Deep learning is used. First, take the input of the given image and preprocessed the given image, and convert the RGB image into a grayscale image. The extracted features can be used for the recognition, classification, and retrieval of currency notes.

• Accuracy is more
• Less distortion rate
• Take very less time to execute

Hardware Requirements
• system
• 4 GB of RAM
• 500 GB of Hard disk



Block Diagram

Fake Currency Detection Using Neural Networks
Fake Currency Detection Using Neural Networks

Fake Currency Detection Using Neural Networks


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[3] Hamid Hassanpour and Payam M. Farahabadi. “Using Hidden Markov models for
paper currency recognition”. In: Expert Systems with Applications 36.6 (2009), pp.
[4] Vipin Kumar Jain and Ritu Vijay. “Indian currency denomination identification
using image processing technique”. In: (2013).
[5] Smart Kotwal. “Image processing based heuristic analysis for enhanced currency
recognition”. In: International Journal of Advancements in Technology 2.1 (2011),
pp. 82–89.

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